Typical intellectual engagement, Big Five personality traits, approaches to learning and cognitive ability predictors of academic performance

Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental vali...

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Published inBritish journal of educational psychology Vol. 79; no. 4; pp. 769 - 782
Main Authors Furnham, Adrian, Monsen, Jeremy, Ahmetoglu, Gorkan
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.12.2009
British Psychological Society
Subjects
Online AccessGet full text
ISSN0007-0998
2044-8279
DOI10.1348/978185409X412147

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Abstract Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. Aims The aim was to determine how much variance ability and non‐ability tests can account for in predicting specific GCSE exam scores. Sample The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Method Pupils completed three self‐report tests: the Neuroticism–Extroversion–Openness‐Five‐Factor Inventory (NEO‐FFI) which measures the ‘Big Five’ personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four ‘core’ compulsory exams as well as a number of specific elective subjects. Results Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Conclusions Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
AbstractList Background: Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. Aims: The aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores. Sample: The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Method: Pupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the 'Big Five' personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four 'core' compulsory exams as well as a number of specific elective subjects. Results: Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Conclusions: Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught. Adapted from the source document.
Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. The aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores. The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Pupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the 'Big Five' personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four 'core' compulsory exams as well as a number of specific elective subjects. Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results.BACKGROUNDBoth ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results.The aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores.AIMSThe aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores.The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years).SAMPLEThe sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years).Pupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the 'Big Five' personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four 'core' compulsory exams as well as a number of specific elective subjects.METHODPupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the 'Big Five' personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four 'core' compulsory exams as well as a number of specific elective subjects.Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors.RESULTSCorrelational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors.Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.CONCLUSIONSData from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. Aims The aim was to determine how much variance ability and non‐ability tests can account for in predicting specific GCSE exam scores. Sample The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Method Pupils completed three self‐report tests: the Neuroticism–Extroversion–Openness‐Five‐Factor Inventory (NEO‐FFI) which measures the ‘Big Five’ personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four ‘core’ compulsory exams as well as a number of specific elective subjects. Results Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Conclusions Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. Aims The aim was to determine how much variance ability and non‐ability tests can account for in predicting specific GCSE exam scores. Sample The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years ( SD 0.98 years). Method Pupils completed three self‐report tests: the Neuroticism–Extroversion–Openness‐Five‐Factor Inventory (NEO‐FFI) which measures the ‘Big Five’ personality traits, ( Costa & McCrae, 1992 ); the Typical Intellectual Engagement Scale ( Goff & Ackerman, 1992 ) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987 ). They also completed two ability tests: the Wonderlic Personnel Test ( Wonderlic, 1992 ) a short measure of general intelligence and the General Knowledge Test ( Irving, Cammock, & Lynn, 2001 ) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four ‘core’ compulsory exams as well as a number of specific elective subjects. Results Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Conclusions Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. The aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores. The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Pupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the 'Big Five' personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four 'core' compulsory exams as well as a number of specific elective subjects. Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Background: Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These include fluid as well as crystalized intelligence, personality traits, and learning styles. This paper examines the incremental validity of five psychometric tests and the sex and age of pupils to predict their General Certificate in Secondary Education (GCSE) test results. Aims: The aim was to determine how much variance ability and non-ability tests can account for in predicting specific GCSE exam scores. Sample: The sample comprised 212 British schoolchildren. Of these, 123 were females. Their mean age was 15.8 years (SD 0.98 years). Method: Pupils completed three self-report tests: the Neuroticism-Extroversion-Openness-Five-Factor Inventory (NEO-FFI) which measures the "Big Five" personality traits, (Costa & McCrae, 1992); the Typical Intellectual Engagement Scale (Goff & Ackerman, 1992) and a measure of learning style, the Study Process Questionnaire (SPQ; Biggs, 1987). They also completed two ability tests: the Wonderlic Personnel Test (Wonderlic, 1992) a short measure of general intelligence and the General Knowledge Test (Irving, Cammock, & Lynn, 2001) a measure of crystallized intelligence. Six months later they took their (10th grade) GCSE exams comprising four "core" compulsory exams as well as a number of specific elective subjects. Results: Correlational analysis suggested that intelligence was the best predictors of school results. Preference test measures accounted for relatively little variance. Regressions indicated that over 50% of the variance in school exams for English (Literature and Language) and Maths and Science combined could be accounted for by these individual difference factors. Conclusions: Data from less than an hour's worth of testing pupils could predict school exam results 6 months later. These tests could, therefore, be used to reliably inform important decisions about how pupils are taught.
Audience High Schools
Grade 10
Author Monsen, Jeremy
Ahmetoglu, Gorkan
Furnham, Adrian
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  fullname: Ahmetoglu, Gorkan
  organization: Department of Psychology, University College London, London, UK
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Issue 4
Keywords Human
Learning
Academic achievement
Acquisition process
Big Five personality model
Secondary education
Adolescent
Cognitive ability
Cognition
Personality
Predictive factor
Performance
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References McCrae, R. R., Costa, P. T. Jr. Personality trait structure as a human universal American Psychologist 52 509-516 1997.
Ackerman, P. L., Bowen, K. R., Beier, M. B., Kanfer, R. Determinants of individual differences and gender differences in knowledge Journal of Educational Psychology 93 797-825 2001.
Bidjerano, T., Dai, D. The relationship between the big-five model of personality and self-regulated learning strategies Learning and Individual Differences 17 69-81 2007.
Chamorro-Premuzic, T., Furnham, A. Intellectual competence and the intelligent personality: A third way in differential psychology Review of General Psychology 10 251-267 2006.
Biggs, J. What do inventories of students' learning processes really measure/ British Journal of Educational Psychology 3 3-19 1993.
Zhang, L. F. Does the big five predict learning approach/ Personality and Individual Differences 34 1431-1446 2003.
Matthews, G., Deary, I. J. Personality traits Cambridge Cambridge University Press 1998.
Rolfhus, E. L., Ackerman, P. L. Self-report knowledge: At the crossroads of ability, interest, and personality Journal of Educational Psychology 88 174-188 1996.
Martin, J., Montgomery, R., Saphian, D. Personality, achievement test scores and high school percentiles as predictors of academic performance across four years of coursework Journal of Research in Personality 40 424-431 2006.
Ferguson, A., Patterson, F. The five factor model of personality Personality and Individual Differences 24 789-796 1998.
Woo, S., Harms, P., Kuncel, N. Integrating personality and intelligence: Typical intellectual engagement and need for cognition Personality and Individual Differences 43 1635-1639 2007.
Ferguson, A. A facet and factor analysis of typical intellectual engagement Social Behaviour and Personality 27 545-562 1999.
Ozer, D. J., Benet-Martinez, V. Personality and the prediction of consequential outcomes Annual Review of Psychology 57 81-821 2006.
Chamorro-Premuzic, T., Furnham, A. Personality predicts academic performance: Evidence from two longitudinal studies on British University students Journal of Research in Personality 37 319-338 2003a.
Furnham, A., Chamorro-Premuzic, T. Personality and intelligence as predictors of statistics examination grades Personality and Individual Differences 37 1013-1022 2004.
Ackerman, P. L. Domain-specific knowledge as the dark matter of adult intelligence: Gf/gc, personality and interest correlates Journal of Gerontology: Psychological Sciences and Social Services 55 69-84 2000.
Arteche, A., Chamorro-Premuzic, T., Ackerman, P., Furnham, A. Typical intellectual engagement as a by product of openness, learning approaches and self-assessed intelligence Educational Psychology 29 357-367 2009.
Harris, D. Factors affecting college grades: A review of the literature, 1930-1937 Psychological Bulletin 37 125-166 1940.
Fox, R., McManus, I., Winder, B. The shortened study process questionnaire British Journal of Educational Psychology 71 511-530 2001.
Wonderlic, E. Wonderlic Personnel Test Illinois Libertyville Press 1992.
Ackerman, P. L., Goff, M. Typical intellectual engagement and personality: Reply to Rocklin (1994) Journal of Educational Psychology 86 150-153 1994.
Chamorro-Premuzic, T., Furnham, A. Personality traits and academic exam performance European Journal of Personality 17 237-250 2003b.
Beier, M. E., Ackerman, P. L. Current events knowledge in adults: An investigation of age, intelligence and non-ability determinants Psychology and Aging 16 615-628 2001.
Rocklin, T. Relation between typical intellectual engagement and openness: Comment on Goff and Ackerman Journal of Educational Psychology 86 145-149 1994.
Biggs, J. The study process questionnaire manual Hawthorn Australian Council for Educational Research 1987.
Digman, J. M. Personality structure: Emergence of the five factor model Annual Review of Psychology 41 417-440 1990.
Chamorro-Premuzic, T., Furnham, A., Ackerman, P. The incremental validity of the typical intellectual engagement scale as predictor of different academic performance measures Journal of Personality Assessment 87 261-264 2006.
O'Connor, M., Paunonen, S. Big five personality predictors of post-secondary academic performance Personality and Individual Differences 43 971-990 2007.
Chamorro-Premuzic, T., Furnham, A. Personality and intellectual competence Mahwah, NJ Erlbaum 2005.
Farsides, T., Woodfield, R. Individual and gender differences in good and first class undergraduate degree performance British Journal of Psychology 98 467-483 2006.
Ackerman, P. L., Rolfhus, E. L. The locus of adult intelligence: Knowledge, abilities, and non-ability traits Psychology and Aging 14 314-330 1999.
Deary, I., Strand, S., Smith, P., Fernandes, C. Intelligence and educational achievement Intelligence 35 13-21 2007.
Furnham, A., Swami, V., Aertche, A., Chamorro-Premuzic, T. Cognitive ability, learning approaches and personality correlates of general knowledge Educational Psychology 28 427-437 2008.
Furnham, A., Chamorro-Premuzic, T., Moutafi, J. Personality and intelligence: gender, the Big Five, self-estimated and psychometric intelligence International Journal of Selection and Assessment 13 11-24 2005.
Cronbach, L. J. Essentials of psychological testing New York Halpern 1949.
Ackerman, P. L., Kanfer, R., Goff, M. Cognitive and noncognitive determinants and consequences of complex skill acquisition Journal of Experimental Psychology: Applied 1 270-304 1995.
Rolfhus, E. L., Ackerman, P. L. Assessing individual differences in knowledge: Knowledge structures and traits Journal of Educational Psychology 91 511-526 1999.
Diseth, A. Personality and approaches to learning as predictors of academic achievement European Journal of Personality 17 143-155 2003.
Chamorro-Premuzic, T., Furnham, A. A possible model to understand the personality-intelligence interface British Journal of Psychology 95 249-264 2004.
Furnham, A., Monsen, J. Personality traits and intelligence predict academic school grades Learning and Individual Differences 19 28-33 2009.
Costa, P. T. Jr., McCrae, R. R. Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI): Professional manual Odessa, FL Psychological Assessment Resources 1992.
Wolf, M., Ackerman, P. Extraversion and intelligence: A meta-analytic investigation Personality and Individual Differences 38 531-542 2005.
Irving, P., Cammock, T., Lynn, R. Some evidence for the existence of a general factor of semantic memory and its components Personality and Individual Differences 30 857-871 2001.
Duff, A., Boyle, E., Dunleavy, K., Ferguson, J. The relationship between personality, approach to learning and academic performance Personality and Individual Differences 36 1907-1920 2004.
Kanfer, R., Ackerman, P. L., Heggestad, E. D. Motivational skills and self-regulation for learning: A trait perspective Learning and Individual Differences 8 185-209 1996.
Landra, K., Pullmann, H., Allick, J. Personality and intelligence as predictors of academic achievement Personality and Individual Differences 42 441-451 2007.
Thompson, R., Zamboanga, B. Academic aptitude and prior knowledge as predictors of student achievement in introduction to psychology Journal of Educational Psychology 96 778-784 2004.
Cattell, R. B. Abilities: Their structure, growth, and action Boston, MA Houghton Mifflin 1971.
Heaven, P., Ciarrochi, J., Vialle, W. Conscientiousness and eysenckian psychoticism as predictors of school grades Personality and Individual Differences 42 535-546 2007.
Chamorro-Premuzic, T., Furnham, A., Lewis, M. Personality and approaches to learning predict preference for different teaching methods Learning and Individual Differences 17 241-250 2007.
Jensen, A. R. Uses of sibling data in educational and psychological research American Educational Research Journal 17 153-170 1980.
Goff, M., Ackerman, P. L. Personality-intelligence relations: Assessment of typical intellectual engagement Journal of Educational Psychology 84 537-553 1992.
2001; 93
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1940; 37
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References_xml – reference: Arteche, A., Chamorro-Premuzic, T., Ackerman, P., Furnham, A. Typical intellectual engagement as a by product of openness, learning approaches and self-assessed intelligence Educational Psychology 29 357-367 2009.
– reference: Rolfhus, E. L., Ackerman, P. L. Self-report knowledge: At the crossroads of ability, interest, and personality Journal of Educational Psychology 88 174-188 1996.
– reference: Furnham, A., Monsen, J. Personality traits and intelligence predict academic school grades Learning and Individual Differences 19 28-33 2009.
– reference: Irving, P., Cammock, T., Lynn, R. Some evidence for the existence of a general factor of semantic memory and its components Personality and Individual Differences 30 857-871 2001.
– reference: Cattell, R. B. Abilities: Their structure, growth, and action Boston, MA Houghton Mifflin 1971.
– reference: Digman, J. M. Personality structure: Emergence of the five factor model Annual Review of Psychology 41 417-440 1990.
– reference: Chamorro-Premuzic, T., Furnham, A., Lewis, M. Personality and approaches to learning predict preference for different teaching methods Learning and Individual Differences 17 241-250 2007.
– reference: Bidjerano, T., Dai, D. The relationship between the big-five model of personality and self-regulated learning strategies Learning and Individual Differences 17 69-81 2007.
– reference: Cronbach, L. J. Essentials of psychological testing New York Halpern 1949.
– reference: Duff, A., Boyle, E., Dunleavy, K., Ferguson, J. The relationship between personality, approach to learning and academic performance Personality and Individual Differences 36 1907-1920 2004.
– reference: Ferguson, A. A facet and factor analysis of typical intellectual engagement Social Behaviour and Personality 27 545-562 1999.
– reference: Jensen, A. R. Uses of sibling data in educational and psychological research American Educational Research Journal 17 153-170 1980.
– reference: Deary, I., Strand, S., Smith, P., Fernandes, C. Intelligence and educational achievement Intelligence 35 13-21 2007.
– reference: Beier, M. E., Ackerman, P. L. Current events knowledge in adults: An investigation of age, intelligence and non-ability determinants Psychology and Aging 16 615-628 2001.
– reference: Kanfer, R., Ackerman, P. L., Heggestad, E. D. Motivational skills and self-regulation for learning: A trait perspective Learning and Individual Differences 8 185-209 1996.
– reference: Landra, K., Pullmann, H., Allick, J. Personality and intelligence as predictors of academic achievement Personality and Individual Differences 42 441-451 2007.
– reference: Wonderlic, E. Wonderlic Personnel Test Illinois Libertyville Press 1992.
– reference: Biggs, J. What do inventories of students' learning processes really measure/ British Journal of Educational Psychology 3 3-19 1993.
– reference: Chamorro-Premuzic, T., Furnham, A. Personality and intellectual competence Mahwah, NJ Erlbaum 2005.
– reference: Ackerman, P. L., Kanfer, R., Goff, M. Cognitive and noncognitive determinants and consequences of complex skill acquisition Journal of Experimental Psychology: Applied 1 270-304 1995.
– reference: Farsides, T., Woodfield, R. Individual and gender differences in good and first class undergraduate degree performance British Journal of Psychology 98 467-483 2006.
– reference: Chamorro-Premuzic, T., Furnham, A. A possible model to understand the personality-intelligence interface British Journal of Psychology 95 249-264 2004.
– reference: Thompson, R., Zamboanga, B. Academic aptitude and prior knowledge as predictors of student achievement in introduction to psychology Journal of Educational Psychology 96 778-784 2004.
– reference: Biggs, J. The study process questionnaire manual Hawthorn Australian Council for Educational Research 1987.
– reference: Ozer, D. J., Benet-Martinez, V. Personality and the prediction of consequential outcomes Annual Review of Psychology 57 81-821 2006.
– reference: Ferguson, A., Patterson, F. The five factor model of personality Personality and Individual Differences 24 789-796 1998.
– reference: Ackerman, P. L., Rolfhus, E. L. The locus of adult intelligence: Knowledge, abilities, and non-ability traits Psychology and Aging 14 314-330 1999.
– reference: Furnham, A., Chamorro-Premuzic, T., Moutafi, J. Personality and intelligence: gender, the Big Five, self-estimated and psychometric intelligence International Journal of Selection and Assessment 13 11-24 2005.
– reference: Zhang, L. F. Does the big five predict learning approach/ Personality and Individual Differences 34 1431-1446 2003.
– reference: Chamorro-Premuzic, T., Furnham, A. Intellectual competence and the intelligent personality: A third way in differential psychology Review of General Psychology 10 251-267 2006.
– reference: Chamorro-Premuzic, T., Furnham, A. Personality predicts academic performance: Evidence from two longitudinal studies on British University students Journal of Research in Personality 37 319-338 2003a.
– reference: Matthews, G., Deary, I. J. Personality traits Cambridge Cambridge University Press 1998.
– reference: McCrae, R. R., Costa, P. T. Jr. Personality trait structure as a human universal American Psychologist 52 509-516 1997.
– reference: O'Connor, M., Paunonen, S. Big five personality predictors of post-secondary academic performance Personality and Individual Differences 43 971-990 2007.
– reference: Ackerman, P. L. Domain-specific knowledge as the dark matter of adult intelligence: Gf/gc, personality and interest correlates Journal of Gerontology: Psychological Sciences and Social Services 55 69-84 2000.
– reference: Fox, R., McManus, I., Winder, B. The shortened study process questionnaire British Journal of Educational Psychology 71 511-530 2001.
– reference: Goff, M., Ackerman, P. L. Personality-intelligence relations: Assessment of typical intellectual engagement Journal of Educational Psychology 84 537-553 1992.
– reference: Chamorro-Premuzic, T., Furnham, A., Ackerman, P. The incremental validity of the typical intellectual engagement scale as predictor of different academic performance measures Journal of Personality Assessment 87 261-264 2006.
– reference: Diseth, A. Personality and approaches to learning as predictors of academic achievement European Journal of Personality 17 143-155 2003.
– reference: Martin, J., Montgomery, R., Saphian, D. Personality, achievement test scores and high school percentiles as predictors of academic performance across four years of coursework Journal of Research in Personality 40 424-431 2006.
– reference: Rolfhus, E. L., Ackerman, P. L. Assessing individual differences in knowledge: Knowledge structures and traits Journal of Educational Psychology 91 511-526 1999.
– reference: Wolf, M., Ackerman, P. Extraversion and intelligence: A meta-analytic investigation Personality and Individual Differences 38 531-542 2005.
– reference: Heaven, P., Ciarrochi, J., Vialle, W. Conscientiousness and eysenckian psychoticism as predictors of school grades Personality and Individual Differences 42 535-546 2007.
– reference: Harris, D. Factors affecting college grades: A review of the literature, 1930-1937 Psychological Bulletin 37 125-166 1940.
– reference: Furnham, A., Chamorro-Premuzic, T. Personality and intelligence as predictors of statistics examination grades Personality and Individual Differences 37 1013-1022 2004.
– reference: Rocklin, T. Relation between typical intellectual engagement and openness: Comment on Goff and Ackerman Journal of Educational Psychology 86 145-149 1994.
– reference: Ackerman, P. L., Goff, M. Typical intellectual engagement and personality: Reply to Rocklin (1994) Journal of Educational Psychology 86 150-153 1994.
– reference: Ackerman, P. L., Bowen, K. R., Beier, M. B., Kanfer, R. Determinants of individual differences and gender differences in knowledge Journal of Educational Psychology 93 797-825 2001.
– reference: Costa, P. T. Jr., McCrae, R. R. Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI): Professional manual Odessa, FL Psychological Assessment Resources 1992.
– reference: Chamorro-Premuzic, T., Furnham, A. Personality traits and academic exam performance European Journal of Personality 17 237-250 2003b.
– reference: Woo, S., Harms, P., Kuncel, N. Integrating personality and intelligence: Typical intellectual engagement and need for cognition Personality and Individual Differences 43 1635-1639 2007.
– reference: Furnham, A., Swami, V., Aertche, A., Chamorro-Premuzic, T. Cognitive ability, learning approaches and personality correlates of general knowledge Educational Psychology 28 427-437 2008.
– volume: 30
  start-page: 857
  year: 2001
  end-page: 871
  article-title: Some evidence for the existence of a general factor of semantic memory and its components
  publication-title: Personality and Individual Differences
– volume: 96
  start-page: 778
  year: 2004
  end-page: 784
  article-title: Academic aptitude and prior knowledge as predictors of student achievement in introduction to psychology
  publication-title: Journal of Educational Psychology
– year: 2005
– volume: 86
  start-page: 150
  year: 1994
  end-page: 153
  article-title: Typical intellectual engagement and personality: Reply to Rocklin (1994)
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Snippet Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school...
Background Both ability (measured by power tests) and non‐ability (measured by preference tests) individual difference measures predict academic school...
Background: Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school...
Both ability (measured by power tests) and non-ability (measured by preference tests) individual difference measures predict academic school outcomes. These...
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SubjectTerms Academic achievement
Achievement
Adolescent
Aptitude
Biological and medical sciences
Character
Cognitive Ability
Cognitive Style
Correlation
Correlation analysis
Educational evaluation
Educational psychology
Elective Courses
England
Female
Five factor model
Foreign Countries
Fundamental and applied biological sciences. Psychology
Grade 10
High School Students
Humans
Individual Differences
Individuality
Intelligence
Intelligence Tests
Learning
Learning styles
Male
Occupational Tests
Personality
Personality Inventory - statistics & numerical data
Personality Traits
Predictor Variables
Preferences
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Psychometrics - statistics & numerical data
Pupil and student. Academic achievement and failure
Pupils
Questionnaires
Reproducibility of Results
Secondary Education
Secondary school students
Standardized Tests
Study Process Questionnaire (Biggs)
Test Results
Testing
United Kingdom
Wonderlic Personnel Test
Title Typical intellectual engagement, Big Five personality traits, approaches to learning and cognitive ability predictors of academic performance
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Volume 79
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